[1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.05 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.2 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.4 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.6 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.8 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.95
[1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.05 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.2 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.4 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.6 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.8 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.95
[1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.05 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.2 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.4 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.6 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.8 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.95
[1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.05 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.2 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.4 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.6 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.8 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.95
[1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.05 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.2 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.4 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.6 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.8 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.95
[1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.05 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.2 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.4 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.6 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.8 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.95
[1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.05 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.2 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.4 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.6 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.8 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.95
[1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.05 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.2 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.4 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.6 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.8 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.95
[1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.05 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.2 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.4 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.6 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.8 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.95
[1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.05 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.2 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.4 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.6 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.8 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.95
[1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.05 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.2 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.4 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.6 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.8 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.95
[1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.05 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.2 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.4 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.6 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.8 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.95
[1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.05 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.2 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.4 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.6 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.8 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.95
[1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.05 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.2 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.4 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.6 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.8 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.95
[1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.05 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.2 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.4 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.6 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.8 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.95
[1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.05 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.2 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.4 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.6 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.8 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.95
[1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.05 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.2 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.4 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.6 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.8 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.95
[1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.05 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.2 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.4 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.6 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.8 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.95
[1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.05 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.2 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.4 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.6 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.8 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.95
[1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.05 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.2 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.4 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.6 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.8 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.95
[1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.05 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.2 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.4 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.6 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.8 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.95
[1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.05 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.2 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.4 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.6 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.8 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.95
[1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.05 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.2 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.4 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.6 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.8 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.95
[1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.05 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.2 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.4 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.6 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.8 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.95

Printing the
analysis outputs
par(op)
par(mfrow=c(1,2),cex=0.6)
rownames(totBaM) <- thenames
rownames(totDeM) <- thenames
rownames(toUnmatM) <- thenames
rownames(unalteredM) <- thenames
rownames(Decorrleated_FractionM) <- thenames
rownames(Base_FractionM) <- thenames
rownames(Unaltered_FractionM) <- thenames
rownames(sparcityM) <- thenames
rownames(Average_Latent_SizeM) <- thenames
rownames(SigDeM) <- thenames
rownames(La_SignificantM) <- thenames
rownames(pbKNNaucM) <- thenames
rownames(pbKNNaccM) <- thenames
colnames(totBaM) <- thr
colnames(totDeM) <- thr
colnames(toUnmatM) <- thr
colnames(unalteredM) <- thr
colnames(Decorrleated_FractionM) <- thr
colnames(Base_FractionM) <- thr
colnames(Unaltered_FractionM) <- thr
colnames(sparcityM) <- thr
colnames(Average_Latent_SizeM) <- thr
colnames(SigDeM) <- thr
colnames(La_SignificantM) <- thr
colnames(pbKNNaucM) <- thr
colnames(pbKNNaccM) <- thr
pander::pander(totFe)
753
pander::pander(totBaM)
| T_Blind_fast_LM_FALSE |
1 |
3 |
36 |
55 |
74 |
114 |
| T_Blind_fast_LM_TRUE |
6 |
1 |
22 |
39 |
49 |
104 |
| T_Blind_fast_RLM_FALSE |
1 |
3 |
36 |
55 |
74 |
114 |
| T_Blind_fast_RLM_TRUE |
6 |
1 |
22 |
39 |
49 |
104 |
| T_Blind_pearson_LM_FALSE |
1 |
3 |
35 |
55 |
74 |
114 |
| T_Blind_pearson_LM_TRUE |
1 |
5 |
22 |
39 |
49 |
104 |
| T_Blind_pearson_RLM_FALSE |
29 |
20 |
41 |
55 |
69 |
114 |
| T_Blind_pearson_RLM_TRUE |
39 |
34 |
42 |
36 |
48 |
104 |
| T_Blind_spearman_LM_FALSE |
9 |
8 |
30 |
40 |
66 |
103 |
| T_Blind_spearman_LM_TRUE |
9 |
5 |
29 |
31 |
49 |
86 |
| T_Blind_spearman_RLM_FALSE |
17 |
13 |
24 |
40 |
65 |
99 |
| T_Blind_spearman_RLM_TRUE |
16 |
10 |
18 |
28 |
46 |
84 |
| T_Driven_fast_LM_FALSE |
19 |
20 |
34 |
57 |
71 |
116 |
| T_Driven_fast_LM_TRUE |
11 |
13 |
30 |
44 |
52 |
106 |
| T_Driven_fast_RLM_FALSE |
19 |
20 |
34 |
57 |
71 |
116 |
| T_Driven_fast_RLM_TRUE |
11 |
13 |
30 |
44 |
52 |
106 |
| T_Driven_pearson_LM_FALSE |
18 |
18 |
34 |
57 |
71 |
116 |
| T_Driven_pearson_LM_TRUE |
8 |
9 |
31 |
44 |
52 |
106 |
| T_Driven_pearson_RLM_FALSE |
30 |
35 |
52 |
64 |
71 |
118 |
| T_Driven_pearson_RLM_TRUE |
29 |
41 |
47 |
53 |
57 |
108 |
| T_Driven_spearman_LM_FALSE |
29 |
22 |
35 |
41 |
66 |
106 |
| T_Driven_spearman_LM_TRUE |
21 |
18 |
31 |
39 |
54 |
92 |
| T_Driven_spearman_RLM_FALSE |
26 |
28 |
36 |
45 |
65 |
102 |
| T_Driven_spearman_RLM_TRUE |
17 |
21 |
33 |
37 |
48 |
89 |
pander::pander(totDeM)
| T_Blind_fast_LM_FALSE |
751 |
749 |
648 |
571 |
483 |
306 |
| T_Blind_fast_LM_TRUE |
737 |
751 |
665 |
590 |
507 |
315 |
| T_Blind_fast_RLM_FALSE |
751 |
749 |
648 |
571 |
483 |
306 |
| T_Blind_fast_RLM_TRUE |
737 |
751 |
665 |
590 |
507 |
315 |
| T_Blind_pearson_LM_FALSE |
751 |
749 |
648 |
571 |
483 |
306 |
| T_Blind_pearson_LM_TRUE |
751 |
712 |
665 |
590 |
507 |
315 |
| T_Blind_pearson_RLM_FALSE |
596 |
623 |
596 |
553 |
477 |
304 |
| T_Blind_pearson_RLM_TRUE |
574 |
589 |
573 |
564 |
498 |
310 |
| T_Blind_spearman_LM_FALSE |
638 |
625 |
596 |
564 |
483 |
336 |
| T_Blind_spearman_LM_TRUE |
628 |
650 |
593 |
574 |
500 |
356 |
| T_Blind_spearman_RLM_FALSE |
599 |
602 |
610 |
569 |
485 |
335 |
| T_Blind_spearman_RLM_TRUE |
614 |
615 |
618 |
581 |
500 |
355 |
| T_Driven_fast_LM_FALSE |
699 |
695 |
649 |
569 |
486 |
303 |
| T_Driven_fast_LM_TRUE |
684 |
692 |
658 |
585 |
506 |
313 |
| T_Driven_fast_RLM_FALSE |
699 |
695 |
649 |
569 |
486 |
303 |
| T_Driven_fast_RLM_TRUE |
684 |
692 |
658 |
585 |
506 |
313 |
| T_Driven_pearson_LM_FALSE |
695 |
705 |
649 |
569 |
486 |
303 |
| T_Driven_pearson_LM_TRUE |
697 |
711 |
655 |
585 |
506 |
313 |
| T_Driven_pearson_RLM_FALSE |
606 |
594 |
570 |
535 |
471 |
297 |
| T_Driven_pearson_RLM_TRUE |
601 |
579 |
569 |
546 |
486 |
303 |
| T_Driven_spearman_LM_FALSE |
586 |
602 |
590 |
565 |
486 |
333 |
| T_Driven_spearman_LM_TRUE |
600 |
613 |
601 |
575 |
495 |
352 |
| T_Driven_spearman_RLM_FALSE |
583 |
581 |
592 |
566 |
486 |
334 |
| T_Driven_spearman_RLM_TRUE |
606 |
609 |
595 |
571 |
498 |
351 |
pander::pander(toUnmatM)
| T_Blind_fast_LM_FALSE |
1 |
3 |
36 |
55 |
74 |
114 |
| T_Blind_fast_LM_TRUE |
6 |
1 |
22 |
39 |
49 |
104 |
| T_Blind_fast_RLM_FALSE |
1 |
3 |
36 |
55 |
74 |
114 |
| T_Blind_fast_RLM_TRUE |
6 |
1 |
22 |
39 |
49 |
104 |
| T_Blind_pearson_LM_FALSE |
1 |
3 |
35 |
55 |
74 |
114 |
| T_Blind_pearson_LM_TRUE |
1 |
5 |
22 |
39 |
49 |
104 |
| T_Blind_pearson_RLM_FALSE |
29 |
20 |
41 |
55 |
69 |
114 |
| T_Blind_pearson_RLM_TRUE |
39 |
34 |
42 |
36 |
48 |
104 |
| T_Blind_spearman_LM_FALSE |
9 |
8 |
30 |
40 |
66 |
103 |
| T_Blind_spearman_LM_TRUE |
9 |
5 |
29 |
31 |
49 |
86 |
| T_Blind_spearman_RLM_FALSE |
17 |
13 |
24 |
40 |
65 |
99 |
| T_Blind_spearman_RLM_TRUE |
16 |
10 |
18 |
28 |
46 |
84 |
| T_Driven_fast_LM_FALSE |
19 |
20 |
34 |
57 |
71 |
116 |
| T_Driven_fast_LM_TRUE |
11 |
13 |
30 |
44 |
52 |
106 |
| T_Driven_fast_RLM_FALSE |
19 |
20 |
34 |
57 |
71 |
116 |
| T_Driven_fast_RLM_TRUE |
11 |
13 |
30 |
44 |
52 |
106 |
| T_Driven_pearson_LM_FALSE |
18 |
18 |
34 |
57 |
71 |
116 |
| T_Driven_pearson_LM_TRUE |
8 |
9 |
31 |
44 |
52 |
106 |
| T_Driven_pearson_RLM_FALSE |
30 |
35 |
52 |
64 |
71 |
118 |
| T_Driven_pearson_RLM_TRUE |
29 |
41 |
47 |
53 |
57 |
108 |
| T_Driven_spearman_LM_FALSE |
29 |
22 |
35 |
41 |
66 |
106 |
| T_Driven_spearman_LM_TRUE |
21 |
18 |
31 |
39 |
54 |
92 |
| T_Driven_spearman_RLM_FALSE |
26 |
28 |
36 |
45 |
65 |
102 |
| T_Driven_spearman_RLM_TRUE |
17 |
21 |
33 |
37 |
48 |
89 |
pander::pander(unalteredM)
| T_Blind_fast_LM_FALSE |
2 |
4 |
105 |
182 |
270 |
447 |
| T_Blind_fast_LM_TRUE |
16 |
2 |
88 |
163 |
246 |
438 |
| T_Blind_fast_RLM_FALSE |
2 |
4 |
105 |
182 |
270 |
447 |
| T_Blind_fast_RLM_TRUE |
16 |
2 |
88 |
163 |
246 |
438 |
| T_Blind_pearson_LM_FALSE |
2 |
4 |
105 |
182 |
270 |
447 |
| T_Blind_pearson_LM_TRUE |
2 |
41 |
88 |
163 |
246 |
438 |
| T_Blind_pearson_RLM_FALSE |
157 |
130 |
157 |
200 |
276 |
449 |
| T_Blind_pearson_RLM_TRUE |
179 |
164 |
180 |
189 |
255 |
443 |
| T_Blind_spearman_LM_FALSE |
115 |
128 |
157 |
189 |
270 |
417 |
| T_Blind_spearman_LM_TRUE |
125 |
103 |
160 |
179 |
253 |
397 |
| T_Blind_spearman_RLM_FALSE |
154 |
151 |
143 |
184 |
268 |
418 |
| T_Blind_spearman_RLM_TRUE |
139 |
138 |
135 |
172 |
253 |
398 |
| T_Driven_fast_LM_FALSE |
54 |
58 |
104 |
184 |
267 |
450 |
| T_Driven_fast_LM_TRUE |
69 |
61 |
95 |
168 |
247 |
440 |
| T_Driven_fast_RLM_FALSE |
54 |
58 |
104 |
184 |
267 |
450 |
| T_Driven_fast_RLM_TRUE |
69 |
61 |
95 |
168 |
247 |
440 |
| T_Driven_pearson_LM_FALSE |
58 |
48 |
104 |
184 |
267 |
450 |
| T_Driven_pearson_LM_TRUE |
56 |
42 |
98 |
168 |
247 |
440 |
| T_Driven_pearson_RLM_FALSE |
147 |
159 |
183 |
218 |
282 |
456 |
| T_Driven_pearson_RLM_TRUE |
152 |
174 |
184 |
207 |
267 |
450 |
| T_Driven_spearman_LM_FALSE |
167 |
151 |
163 |
188 |
267 |
420 |
| T_Driven_spearman_LM_TRUE |
153 |
140 |
152 |
178 |
258 |
401 |
| T_Driven_spearman_RLM_FALSE |
170 |
172 |
161 |
187 |
267 |
419 |
| T_Driven_spearman_RLM_TRUE |
147 |
144 |
158 |
182 |
255 |
402 |
pander::pander(Decorrleated_FractionM)
| T_Blind_fast_LM_FALSE |
0.997 |
0.995 |
0.861 |
0.758 |
0.641 |
0.406 |
| T_Blind_fast_LM_TRUE |
0.979 |
0.997 |
0.883 |
0.784 |
0.673 |
0.418 |
| T_Blind_fast_RLM_FALSE |
0.997 |
0.995 |
0.861 |
0.758 |
0.641 |
0.406 |
| T_Blind_fast_RLM_TRUE |
0.979 |
0.997 |
0.883 |
0.784 |
0.673 |
0.418 |
| T_Blind_pearson_LM_FALSE |
0.997 |
0.995 |
0.861 |
0.758 |
0.641 |
0.406 |
| T_Blind_pearson_LM_TRUE |
0.997 |
0.946 |
0.883 |
0.784 |
0.673 |
0.418 |
| T_Blind_pearson_RLM_FALSE |
0.792 |
0.827 |
0.792 |
0.734 |
0.633 |
0.404 |
| T_Blind_pearson_RLM_TRUE |
0.762 |
0.782 |
0.761 |
0.749 |
0.661 |
0.412 |
| T_Blind_spearman_LM_FALSE |
0.847 |
0.830 |
0.792 |
0.749 |
0.641 |
0.446 |
| T_Blind_spearman_LM_TRUE |
0.834 |
0.863 |
0.788 |
0.762 |
0.664 |
0.473 |
| T_Blind_spearman_RLM_FALSE |
0.795 |
0.799 |
0.810 |
0.756 |
0.644 |
0.445 |
| T_Blind_spearman_RLM_TRUE |
0.815 |
0.817 |
0.821 |
0.772 |
0.664 |
0.471 |
| T_Driven_fast_LM_FALSE |
0.928 |
0.923 |
0.862 |
0.756 |
0.645 |
0.402 |
| T_Driven_fast_LM_TRUE |
0.908 |
0.919 |
0.874 |
0.777 |
0.672 |
0.416 |
| T_Driven_fast_RLM_FALSE |
0.928 |
0.923 |
0.862 |
0.756 |
0.645 |
0.402 |
| T_Driven_fast_RLM_TRUE |
0.908 |
0.919 |
0.874 |
0.777 |
0.672 |
0.416 |
| T_Driven_pearson_LM_FALSE |
0.923 |
0.936 |
0.862 |
0.756 |
0.645 |
0.402 |
| T_Driven_pearson_LM_TRUE |
0.926 |
0.944 |
0.870 |
0.777 |
0.672 |
0.416 |
| T_Driven_pearson_RLM_FALSE |
0.805 |
0.789 |
0.757 |
0.710 |
0.625 |
0.394 |
| T_Driven_pearson_RLM_TRUE |
0.798 |
0.769 |
0.756 |
0.725 |
0.645 |
0.402 |
| T_Driven_spearman_LM_FALSE |
0.778 |
0.799 |
0.784 |
0.750 |
0.645 |
0.442 |
| T_Driven_spearman_LM_TRUE |
0.797 |
0.814 |
0.798 |
0.764 |
0.657 |
0.467 |
| T_Driven_spearman_RLM_FALSE |
0.774 |
0.772 |
0.786 |
0.752 |
0.645 |
0.444 |
| T_Driven_spearman_RLM_TRUE |
0.805 |
0.809 |
0.790 |
0.758 |
0.661 |
0.466 |
pander::pander(Base_FractionM)
| T_Blind_fast_LM_FALSE |
0.00133 |
0.00398 |
0.0478 |
0.0730 |
0.0983 |
0.151 |
| T_Blind_fast_LM_TRUE |
0.00797 |
0.00133 |
0.0292 |
0.0518 |
0.0651 |
0.138 |
| T_Blind_fast_RLM_FALSE |
0.00133 |
0.00398 |
0.0478 |
0.0730 |
0.0983 |
0.151 |
| T_Blind_fast_RLM_TRUE |
0.00797 |
0.00133 |
0.0292 |
0.0518 |
0.0651 |
0.138 |
| T_Blind_pearson_LM_FALSE |
0.00133 |
0.00398 |
0.0465 |
0.0730 |
0.0983 |
0.151 |
| T_Blind_pearson_LM_TRUE |
0.00133 |
0.00664 |
0.0292 |
0.0518 |
0.0651 |
0.138 |
| T_Blind_pearson_RLM_FALSE |
0.03851 |
0.02656 |
0.0544 |
0.0730 |
0.0916 |
0.151 |
| T_Blind_pearson_RLM_TRUE |
0.05179 |
0.04515 |
0.0558 |
0.0478 |
0.0637 |
0.138 |
| T_Blind_spearman_LM_FALSE |
0.01195 |
0.01062 |
0.0398 |
0.0531 |
0.0876 |
0.137 |
| T_Blind_spearman_LM_TRUE |
0.01195 |
0.00664 |
0.0385 |
0.0412 |
0.0651 |
0.114 |
| T_Blind_spearman_RLM_FALSE |
0.02258 |
0.01726 |
0.0319 |
0.0531 |
0.0863 |
0.131 |
| T_Blind_spearman_RLM_TRUE |
0.02125 |
0.01328 |
0.0239 |
0.0372 |
0.0611 |
0.112 |
| T_Driven_fast_LM_FALSE |
0.02523 |
0.02656 |
0.0452 |
0.0757 |
0.0943 |
0.154 |
| T_Driven_fast_LM_TRUE |
0.01461 |
0.01726 |
0.0398 |
0.0584 |
0.0691 |
0.141 |
| T_Driven_fast_RLM_FALSE |
0.02523 |
0.02656 |
0.0452 |
0.0757 |
0.0943 |
0.154 |
| T_Driven_fast_RLM_TRUE |
0.01461 |
0.01726 |
0.0398 |
0.0584 |
0.0691 |
0.141 |
| T_Driven_pearson_LM_FALSE |
0.02390 |
0.02390 |
0.0452 |
0.0757 |
0.0943 |
0.154 |
| T_Driven_pearson_LM_TRUE |
0.01062 |
0.01195 |
0.0412 |
0.0584 |
0.0691 |
0.141 |
| T_Driven_pearson_RLM_FALSE |
0.03984 |
0.04648 |
0.0691 |
0.0850 |
0.0943 |
0.157 |
| T_Driven_pearson_RLM_TRUE |
0.03851 |
0.05445 |
0.0624 |
0.0704 |
0.0757 |
0.143 |
| T_Driven_spearman_LM_FALSE |
0.03851 |
0.02922 |
0.0465 |
0.0544 |
0.0876 |
0.141 |
| T_Driven_spearman_LM_TRUE |
0.02789 |
0.02390 |
0.0412 |
0.0518 |
0.0717 |
0.122 |
| T_Driven_spearman_RLM_FALSE |
0.03453 |
0.03718 |
0.0478 |
0.0598 |
0.0863 |
0.135 |
| T_Driven_spearman_RLM_TRUE |
0.02258 |
0.02789 |
0.0438 |
0.0491 |
0.0637 |
0.118 |
pander::pander(Unaltered_FractionM)
| T_Blind_fast_LM_FALSE |
0.00266 |
0.00531 |
0.139 |
0.242 |
0.359 |
0.594 |
| T_Blind_fast_LM_TRUE |
0.02125 |
0.00266 |
0.117 |
0.216 |
0.327 |
0.582 |
| T_Blind_fast_RLM_FALSE |
0.00266 |
0.00531 |
0.139 |
0.242 |
0.359 |
0.594 |
| T_Blind_fast_RLM_TRUE |
0.02125 |
0.00266 |
0.117 |
0.216 |
0.327 |
0.582 |
| T_Blind_pearson_LM_FALSE |
0.00266 |
0.00531 |
0.139 |
0.242 |
0.359 |
0.594 |
| T_Blind_pearson_LM_TRUE |
0.00266 |
0.05445 |
0.117 |
0.216 |
0.327 |
0.582 |
| T_Blind_pearson_RLM_FALSE |
0.20850 |
0.17264 |
0.208 |
0.266 |
0.367 |
0.596 |
| T_Blind_pearson_RLM_TRUE |
0.23772 |
0.21780 |
0.239 |
0.251 |
0.339 |
0.588 |
| T_Blind_spearman_LM_FALSE |
0.15272 |
0.16999 |
0.208 |
0.251 |
0.359 |
0.554 |
| T_Blind_spearman_LM_TRUE |
0.16600 |
0.13679 |
0.212 |
0.238 |
0.336 |
0.527 |
| T_Blind_spearman_RLM_FALSE |
0.20452 |
0.20053 |
0.190 |
0.244 |
0.356 |
0.555 |
| T_Blind_spearman_RLM_TRUE |
0.18459 |
0.18327 |
0.179 |
0.228 |
0.336 |
0.529 |
| T_Driven_fast_LM_FALSE |
0.07171 |
0.07703 |
0.138 |
0.244 |
0.355 |
0.598 |
| T_Driven_fast_LM_TRUE |
0.09163 |
0.08101 |
0.126 |
0.223 |
0.328 |
0.584 |
| T_Driven_fast_RLM_FALSE |
0.07171 |
0.07703 |
0.138 |
0.244 |
0.355 |
0.598 |
| T_Driven_fast_RLM_TRUE |
0.09163 |
0.08101 |
0.126 |
0.223 |
0.328 |
0.584 |
| T_Driven_pearson_LM_FALSE |
0.07703 |
0.06375 |
0.138 |
0.244 |
0.355 |
0.598 |
| T_Driven_pearson_LM_TRUE |
0.07437 |
0.05578 |
0.130 |
0.223 |
0.328 |
0.584 |
| T_Driven_pearson_RLM_FALSE |
0.19522 |
0.21116 |
0.243 |
0.290 |
0.375 |
0.606 |
| T_Driven_pearson_RLM_TRUE |
0.20186 |
0.23108 |
0.244 |
0.275 |
0.355 |
0.598 |
| T_Driven_spearman_LM_FALSE |
0.22178 |
0.20053 |
0.216 |
0.250 |
0.355 |
0.558 |
| T_Driven_spearman_LM_TRUE |
0.20319 |
0.18592 |
0.202 |
0.236 |
0.343 |
0.533 |
| T_Driven_spearman_RLM_FALSE |
0.22576 |
0.22842 |
0.214 |
0.248 |
0.355 |
0.556 |
| T_Driven_spearman_RLM_TRUE |
0.19522 |
0.19124 |
0.210 |
0.242 |
0.339 |
0.534 |
pander::pander(sparcityM)
| T_Blind_fast_LM_FALSE |
0.85486 |
0.83684 |
0.02432 |
0.00418 |
0.00254 |
0.00199 |
| T_Blind_fast_LM_TRUE |
0.14325 |
0.95355 |
0.03543 |
0.00480 |
0.00279 |
0.00206 |
| T_Blind_fast_RLM_FALSE |
0.85486 |
0.83684 |
0.02432 |
0.00418 |
0.00254 |
0.00199 |
| T_Blind_fast_RLM_TRUE |
0.14325 |
0.95355 |
0.03543 |
0.00480 |
0.00279 |
0.00206 |
| T_Blind_pearson_LM_FALSE |
0.85022 |
0.86646 |
0.02609 |
0.00418 |
0.00254 |
0.00199 |
| T_Blind_pearson_LM_TRUE |
0.72133 |
0.05020 |
0.03543 |
0.00480 |
0.00279 |
0.00206 |
| T_Blind_pearson_RLM_FALSE |
0.00395 |
0.00452 |
0.00387 |
0.00331 |
0.00272 |
0.00213 |
| T_Blind_pearson_RLM_TRUE |
0.00357 |
0.00425 |
0.00507 |
0.00352 |
0.00317 |
0.00214 |
| T_Blind_spearman_LM_FALSE |
0.00640 |
0.00609 |
0.00505 |
0.00377 |
0.00272 |
0.00209 |
| T_Blind_spearman_LM_TRUE |
0.00563 |
0.01403 |
0.00540 |
0.00458 |
0.00291 |
0.00220 |
| T_Blind_spearman_RLM_FALSE |
0.00435 |
0.00469 |
0.00590 |
0.00365 |
0.00257 |
0.00204 |
| T_Blind_spearman_RLM_TRUE |
0.00429 |
0.00508 |
0.00747 |
0.00396 |
0.00268 |
0.00214 |
| T_Driven_fast_LM_FALSE |
0.03861 |
0.03560 |
0.02301 |
0.00428 |
0.00256 |
0.00198 |
| T_Driven_fast_LM_TRUE |
0.01492 |
0.02229 |
0.03427 |
0.00481 |
0.00277 |
0.00204 |
| T_Driven_fast_RLM_FALSE |
0.03861 |
0.03560 |
0.02301 |
0.00428 |
0.00256 |
0.00198 |
| T_Driven_fast_RLM_TRUE |
0.01492 |
0.02229 |
0.03427 |
0.00481 |
0.00277 |
0.00204 |
| T_Driven_pearson_LM_FALSE |
0.03346 |
0.04483 |
0.02314 |
0.00428 |
0.00256 |
0.00198 |
| T_Driven_pearson_LM_TRUE |
0.01596 |
0.03711 |
0.03055 |
0.00481 |
0.00277 |
0.00204 |
| T_Driven_pearson_RLM_FALSE |
0.00404 |
0.00390 |
0.00349 |
0.00305 |
0.00262 |
0.00204 |
| T_Driven_pearson_RLM_TRUE |
0.00366 |
0.00349 |
0.00335 |
0.00318 |
0.00282 |
0.00206 |
| T_Driven_spearman_LM_FALSE |
0.00465 |
0.00505 |
0.00540 |
0.00396 |
0.00281 |
0.00211 |
| T_Driven_spearman_LM_TRUE |
0.00468 |
0.00541 |
0.00544 |
0.00472 |
0.00290 |
0.00216 |
| T_Driven_spearman_RLM_FALSE |
0.00417 |
0.00410 |
0.00516 |
0.00387 |
0.00255 |
0.00205 |
| T_Driven_spearman_RLM_TRUE |
0.00461 |
0.00533 |
0.00526 |
0.00370 |
0.00269 |
0.00211 |
pander::pander(Average_Latent_SizeM)
| T_Blind_fast_LM_FALSE |
664.00 |
627.00 |
2.50 |
2.00 |
2.29 |
2.00 |
| T_Blind_fast_LM_TRUE |
8.00 |
726.00 |
8.00 |
2.00 |
2.08 |
2.00 |
| T_Blind_fast_RLM_FALSE |
664.00 |
627.00 |
2.50 |
2.00 |
2.29 |
2.00 |
| T_Blind_fast_RLM_TRUE |
8.00 |
726.00 |
8.00 |
2.00 |
2.08 |
2.00 |
| T_Blind_pearson_LM_FALSE |
602.50 |
650.00 |
3.67 |
2.00 |
2.29 |
2.00 |
| T_Blind_pearson_LM_TRUE |
582.00 |
37.17 |
8.00 |
2.00 |
2.08 |
2.00 |
| T_Blind_pearson_RLM_FALSE |
2.67 |
3.00 |
3.60 |
2.90 |
2.33 |
2.11 |
| T_Blind_pearson_RLM_TRUE |
2.20 |
2.17 |
4.19 |
2.20 |
3.09 |
2.27 |
| T_Blind_spearman_LM_FALSE |
4.00 |
2.50 |
5.00 |
3.00 |
2.23 |
2.00 |
| T_Blind_spearman_LM_TRUE |
2.00 |
3.33 |
2.40 |
3.15 |
2.00 |
2.12 |
| T_Blind_spearman_RLM_FALSE |
3.40 |
3.00 |
8.00 |
2.25 |
2.10 |
2.00 |
| T_Blind_spearman_RLM_TRUE |
3.43 |
2.00 |
6.80 |
3.42 |
2.17 |
2.08 |
| T_Driven_fast_LM_FALSE |
6.00 |
NA |
12.86 |
3.00 |
2.12 |
2.00 |
| T_Driven_fast_LM_TRUE |
NA |
18.00 |
NA |
2.75 |
2.36 |
2.44 |
| T_Driven_fast_RLM_FALSE |
6.00 |
NA |
12.86 |
3.00 |
2.12 |
2.00 |
| T_Driven_fast_RLM_TRUE |
NA |
18.00 |
NA |
2.75 |
2.36 |
2.44 |
| T_Driven_pearson_LM_FALSE |
NA |
NA |
12.86 |
3.00 |
2.12 |
2.00 |
| T_Driven_pearson_LM_TRUE |
10.00 |
15.00 |
5.00 |
2.75 |
2.36 |
2.44 |
| T_Driven_pearson_RLM_FALSE |
3.00 |
3.00 |
2.33 |
2.00 |
2.10 |
2.12 |
| T_Driven_pearson_RLM_TRUE |
2.00 |
NA |
3.00 |
2.00 |
2.25 |
2.58 |
| T_Driven_spearman_LM_FALSE |
2.00 |
5.00 |
2.00 |
2.00 |
2.43 |
2.25 |
| T_Driven_spearman_LM_TRUE |
NA |
2.00 |
2.80 |
2.83 |
2.00 |
2.14 |
| T_Driven_spearman_RLM_FALSE |
NA |
3.17 |
3.00 |
2.33 |
2.18 |
2.20 |
| T_Driven_spearman_RLM_TRUE |
NA |
NA |
3.00 |
3.50 |
2.62 |
2.18 |
pander::pander(SigDeM)
| T_Blind_fast_LM_FALSE |
1 |
1 |
2 |
4 |
7 |
6 |
| T_Blind_fast_LM_TRUE |
1 |
2 |
1 |
2 |
12 |
7 |
| T_Blind_fast_RLM_FALSE |
1 |
1 |
2 |
4 |
7 |
6 |
| T_Blind_fast_RLM_TRUE |
1 |
2 |
1 |
2 |
12 |
7 |
| T_Blind_pearson_LM_FALSE |
2 |
1 |
3 |
4 |
7 |
6 |
| T_Blind_pearson_LM_TRUE |
1 |
6 |
1 |
2 |
12 |
7 |
| T_Blind_pearson_RLM_FALSE |
9 |
8 |
10 |
10 |
15 |
9 |
| T_Blind_pearson_RLM_TRUE |
5 |
6 |
16 |
5 |
11 |
11 |
| T_Blind_spearman_LM_FALSE |
9 |
2 |
3 |
4 |
13 |
3 |
| T_Blind_spearman_LM_TRUE |
2 |
3 |
5 |
13 |
7 |
8 |
| T_Blind_spearman_RLM_FALSE |
5 |
1 |
2 |
4 |
10 |
7 |
| T_Blind_spearman_RLM_TRUE |
7 |
1 |
5 |
12 |
6 |
13 |
| T_Driven_fast_LM_FALSE |
1 |
0 |
7 |
8 |
8 |
5 |
| T_Driven_fast_LM_TRUE |
0 |
3 |
0 |
8 |
11 |
9 |
| T_Driven_fast_RLM_FALSE |
1 |
0 |
7 |
8 |
8 |
5 |
| T_Driven_fast_RLM_TRUE |
0 |
3 |
0 |
8 |
11 |
9 |
| T_Driven_pearson_LM_FALSE |
0 |
0 |
7 |
8 |
8 |
5 |
| T_Driven_pearson_LM_TRUE |
1 |
1 |
1 |
8 |
11 |
9 |
| T_Driven_pearson_RLM_FALSE |
3 |
9 |
3 |
2 |
10 |
8 |
| T_Driven_pearson_RLM_TRUE |
1 |
0 |
3 |
2 |
8 |
12 |
| T_Driven_spearman_LM_FALSE |
2 |
1 |
1 |
2 |
7 |
4 |
| T_Driven_spearman_LM_TRUE |
0 |
3 |
5 |
6 |
9 |
7 |
| T_Driven_spearman_RLM_FALSE |
0 |
6 |
4 |
9 |
11 |
10 |
| T_Driven_spearman_RLM_TRUE |
0 |
0 |
2 |
6 |
8 |
17 |
pander::pander(La_SignificantM)
| T_Blind_fast_LM_FALSE |
1 |
1 |
2 |
6 |
24 |
65 |
| T_Blind_fast_LM_TRUE |
1 |
2 |
1 |
2 |
31 |
64 |
| T_Blind_fast_RLM_FALSE |
1 |
1 |
2 |
6 |
24 |
65 |
| T_Blind_fast_RLM_TRUE |
1 |
2 |
1 |
2 |
31 |
64 |
| T_Blind_pearson_LM_FALSE |
2 |
1 |
3 |
6 |
24 |
65 |
| T_Blind_pearson_LM_TRUE |
1 |
7 |
1 |
2 |
31 |
64 |
| T_Blind_pearson_RLM_FALSE |
10 |
9 |
13 |
17 |
40 |
70 |
| T_Blind_pearson_RLM_TRUE |
10 |
10 |
22 |
13 |
29 |
70 |
| T_Blind_spearman_LM_FALSE |
9 |
2 |
4 |
10 |
36 |
50 |
| T_Blind_spearman_LM_TRUE |
3 |
4 |
7 |
21 |
24 |
54 |
| T_Blind_spearman_RLM_FALSE |
6 |
1 |
2 |
10 |
29 |
56 |
| T_Blind_spearman_RLM_TRUE |
8 |
3 |
6 |
17 |
20 |
61 |
| T_Driven_fast_LM_FALSE |
2 |
2 |
9 |
17 |
29 |
65 |
| T_Driven_fast_LM_TRUE |
1 |
4 |
1 |
17 |
33 |
72 |
| T_Driven_fast_RLM_FALSE |
2 |
2 |
9 |
17 |
29 |
65 |
| T_Driven_fast_RLM_TRUE |
1 |
4 |
1 |
17 |
33 |
72 |
| T_Driven_pearson_LM_FALSE |
1 |
2 |
9 |
17 |
29 |
65 |
| T_Driven_pearson_LM_TRUE |
2 |
2 |
2 |
17 |
33 |
72 |
| T_Driven_pearson_RLM_FALSE |
5 |
14 |
8 |
9 |
36 |
70 |
| T_Driven_pearson_RLM_TRUE |
2 |
3 |
8 |
9 |
29 |
75 |
| T_Driven_spearman_LM_FALSE |
3 |
2 |
3 |
8 |
24 |
54 |
| T_Driven_spearman_LM_TRUE |
1 |
4 |
7 |
14 |
31 |
56 |
| T_Driven_spearman_RLM_FALSE |
1 |
8 |
6 |
16 |
33 |
62 |
| T_Driven_spearman_RLM_TRUE |
1 |
1 |
4 |
14 |
25 |
69 |
pander::pander(pbKNNaucM)
| T_Blind_fast_LM_FALSE |
0.408 |
0.607 |
0.652 |
0.758 |
0.841 |
0.827 |
| T_Blind_fast_LM_TRUE |
0.617 |
0.503 |
0.595 |
0.694 |
0.838 |
0.802 |
| T_Blind_fast_RLM_FALSE |
0.408 |
0.607 |
0.652 |
0.758 |
0.841 |
0.827 |
| T_Blind_fast_RLM_TRUE |
0.617 |
0.503 |
0.595 |
0.694 |
0.838 |
0.802 |
| T_Blind_pearson_LM_FALSE |
0.498 |
0.504 |
0.695 |
0.758 |
0.841 |
0.827 |
| T_Blind_pearson_LM_TRUE |
0.600 |
0.720 |
0.595 |
0.694 |
0.838 |
0.802 |
| T_Blind_pearson_RLM_FALSE |
0.727 |
0.733 |
0.739 |
0.780 |
0.775 |
0.798 |
| T_Blind_pearson_RLM_TRUE |
0.772 |
0.847 |
0.785 |
0.843 |
0.824 |
0.782 |
| T_Blind_spearman_LM_FALSE |
0.803 |
0.753 |
0.785 |
0.795 |
0.833 |
0.814 |
| T_Blind_spearman_LM_TRUE |
0.746 |
0.776 |
0.775 |
0.797 |
0.825 |
0.831 |
| T_Blind_spearman_RLM_FALSE |
0.738 |
0.504 |
0.623 |
0.794 |
0.805 |
0.737 |
| T_Blind_spearman_RLM_TRUE |
0.671 |
0.739 |
0.741 |
0.770 |
0.784 |
0.785 |
| T_Driven_fast_LM_FALSE |
0.732 |
0.740 |
0.780 |
0.852 |
0.836 |
0.798 |
| T_Driven_fast_LM_TRUE |
0.607 |
0.683 |
0.721 |
0.819 |
0.868 |
0.797 |
| T_Driven_fast_RLM_FALSE |
0.732 |
0.740 |
0.780 |
0.852 |
0.836 |
0.798 |
| T_Driven_fast_RLM_TRUE |
0.607 |
0.683 |
0.721 |
0.819 |
0.868 |
0.797 |
| T_Driven_pearson_LM_FALSE |
0.607 |
0.740 |
0.780 |
0.852 |
0.836 |
0.798 |
| T_Driven_pearson_LM_TRUE |
0.657 |
0.701 |
0.720 |
0.819 |
0.868 |
0.797 |
| T_Driven_pearson_RLM_FALSE |
0.763 |
0.717 |
0.697 |
0.777 |
0.754 |
0.795 |
| T_Driven_pearson_RLM_TRUE |
0.763 |
0.818 |
0.745 |
0.818 |
0.825 |
0.787 |
| T_Driven_spearman_LM_FALSE |
0.759 |
0.797 |
0.783 |
0.838 |
0.843 |
0.797 |
| T_Driven_spearman_LM_TRUE |
0.607 |
0.795 |
0.696 |
0.789 |
0.835 |
0.844 |
| T_Driven_spearman_RLM_FALSE |
0.607 |
0.770 |
0.636 |
0.787 |
0.747 |
0.706 |
| T_Driven_spearman_RLM_TRUE |
0.607 |
0.799 |
0.665 |
0.817 |
0.809 |
0.719 |
pander::pander(pbKNNaccM)
| T_Blind_fast_LM_FALSE |
0.651 |
0.738 |
0.746 |
0.817 |
0.873 |
0.825 |
| T_Blind_fast_LM_TRUE |
0.69 |
0.579 |
0.698 |
0.714 |
0.873 |
0.833 |
| T_Blind_fast_RLM_FALSE |
0.651 |
0.738 |
0.746 |
0.817 |
0.873 |
0.825 |
| T_Blind_fast_RLM_TRUE |
0.69 |
0.579 |
0.698 |
0.714 |
0.873 |
0.833 |
| T_Blind_pearson_LM_FALSE |
0.706 |
0.714 |
0.698 |
0.817 |
0.873 |
0.825 |
| T_Blind_pearson_LM_TRUE |
0.69 |
0.746 |
0.698 |
0.714 |
0.873 |
0.833 |
| T_Blind_pearson_RLM_FALSE |
0.81 |
0.81 |
0.802 |
0.81 |
0.833 |
0.794 |
| T_Blind_pearson_RLM_TRUE |
0.825 |
0.825 |
0.817 |
0.873 |
0.881 |
0.817 |
| T_Blind_spearman_LM_FALSE |
0.794 |
0.73 |
0.825 |
0.857 |
0.865 |
0.833 |
| T_Blind_spearman_LM_TRUE |
0.754 |
0.81 |
0.794 |
0.849 |
0.849 |
0.833 |
| T_Blind_spearman_RLM_FALSE |
0.802 |
0.635 |
0.714 |
0.833 |
0.817 |
0.817 |
| T_Blind_spearman_RLM_TRUE |
0.754 |
0.762 |
0.77 |
0.802 |
0.786 |
0.833 |
| T_Driven_fast_LM_FALSE |
0.746 |
0.794 |
0.794 |
0.865 |
0.873 |
0.817 |
| T_Driven_fast_LM_TRUE |
0.706 |
0.698 |
0.738 |
0.802 |
0.849 |
0.841 |
| T_Driven_fast_RLM_FALSE |
0.746 |
0.794 |
0.794 |
0.865 |
0.873 |
0.817 |
| T_Driven_fast_RLM_TRUE |
0.706 |
0.698 |
0.738 |
0.802 |
0.849 |
0.841 |
| T_Driven_pearson_LM_FALSE |
0.706 |
0.794 |
0.794 |
0.865 |
0.873 |
0.817 |
| T_Driven_pearson_LM_TRUE |
0.706 |
0.786 |
0.833 |
0.802 |
0.849 |
0.841 |
| T_Driven_pearson_RLM_FALSE |
0.802 |
0.817 |
0.802 |
0.817 |
0.77 |
0.817 |
| T_Driven_pearson_RLM_TRUE |
0.802 |
0.802 |
0.778 |
0.802 |
0.865 |
0.825 |
| T_Driven_spearman_LM_FALSE |
0.754 |
0.778 |
0.802 |
0.817 |
0.841 |
0.825 |
| T_Driven_spearman_LM_TRUE |
0.706 |
0.81 |
0.778 |
0.825 |
0.849 |
0.817 |
| T_Driven_spearman_RLM_FALSE |
0.706 |
0.786 |
0.746 |
0.825 |
0.802 |
0.802 |
| T_Driven_spearman_RLM_TRUE |
0.706 |
0.833 |
0.778 |
0.865 |
0.841 |
0.81 |
miny = 0.15
maxy = max(pbKNNaucM)
plot(thr,pbKNNaucM[1,],ylim=c(miny,maxy),
main="KNN's ROCAUC",
xlab="Correlation-Matrix's Maximum Goal",
ylab="ROC AUC",
type="l",
col=1,
lwd=2)
for (ind in 2:nrow(pbKNNaucM))
{
lines(thr,pbKNNaucM[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", rownames(pbKNNaucM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
fastRows <- str_detect(rownames(pbKNNaucM),"fast")
pearsonRows <- str_detect(rownames(pbKNNaucM),"pearson")
spearmanRows <- str_detect(rownames(pbKNNaucM),"spearman")
T_BlindRows <- str_detect(rownames(pbKNNaucM),"T_Blind")
corRankRows <- str_detect(rownames(pbKNNaucM),"TRUE")
maxCorRankRows <- str_detect(rownames(pbKNNaucM),"FALSE")
RLMfitMethod <- str_detect(rownames(pbKNNaucM),"RLM")
meanAuc <- colMeans(pbKNNaucM[fastRows,])
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[pearsonRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[spearmanRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[!T_BlindRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[T_BlindRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[corRankRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[maxCorRankRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[RLMfitMethod,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[!RLMfitMethod,]))
legnames <- c("fast","Pearson","Spearman","T_Driven","T_Blind","SumCor","MaxCor","RLM","LM")
pbKNNaccM <- as.data.frame(pbKNNaccM)
pbKNNaccM[,1:ncol(pbKNNaccM)] <- sapply(pbKNNaccM,as.numeric)
Average_Latent_SizeM <- as.data.frame(Average_Latent_SizeM)
Average_Latent_SizeM[,1:ncol(Average_Latent_SizeM)] <- sapply(Average_Latent_SizeM,as.numeric)
Average_Latent_SizeM[is.na(Average_Latent_SizeM)] <- 0
SigDeM <- as.data.frame(SigDeM)
SigDeM[,1:ncol(SigDeM)] <- sapply(SigDeM,as.numeric)
sparcityM <- as.data.frame(sparcityM)
sparcityM[,1:ncol(sparcityM)] <- sapply(sparcityM,as.numeric)
miny = 0.65
maxy = max(meanAuc)+0.025
plot(thr,meanAuc[1,],ylim=c(miny,maxy),
main="Mean KNN's ROCAUC",
xlab="Correlation-Matrix's Maximum Goal",
ylab="ROC AUC",
type="l",
col=1,
lwd=2,
lty=1)
for (ind in 2:nrow(meanAuc))
{
lines(thr,meanAuc[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = 0.35
maxy = max(pbKNNaccM) + 0.1
plot(thr,pbKNNaccM[1,],ylim=c(miny,maxy),
main="KNN's Accuracy",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Accuracy",
type="l",
col=1,
lwd=2)
for (ind in 2:nrow(pbKNNaucM))
{
lines(thr,pbKNNaccM[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", rownames(pbKNNaucM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
meanAcc <- colMeans(pbKNNaccM[fastRows,])
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[pearsonRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[spearmanRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[!T_BlindRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[T_BlindRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[corRankRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[maxCorRankRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[RLMfitMethod,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[!RLMfitMethod,]))
miny = min(meanAcc)-0.01
maxy = max(meanAcc)+0.025
plot(thr,meanAcc[1,],ylim=c(miny,maxy),
main="Mean KNN's Accuracy",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Accuracy",
type="l",
col=1,
lwd=2)
for (ind in 2:nrow(meanAcc))
{
lines(thr,meanAcc[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = 1
maxy = 20*max(Average_Latent_SizeM)
plot(thr,Average_Latent_SizeM[1,],ylim=c(miny,maxy),
main="Average Size of Latent-Variable",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Size",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(Average_Latent_SizeM))
{
lines(thr,Average_Latent_SizeM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(Average_Latent_SizeM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
meanAccAvgSize <- colMeans(Average_Latent_SizeM[fastRows,])
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[pearsonRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[spearmanRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[!T_BlindRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[T_BlindRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[corRankRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[maxCorRankRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[RLMfitMethod,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[!RLMfitMethod,]))
miny =1
maxy = 5*max(meanAccAvgSize)
plot(thr,meanAccAvgSize[1,],ylim=c(miny,maxy),
main="Mean Size of Average-Latent-Variable",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Size",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(meanAccAvgSize))
{
lines(thr,meanAccAvgSize[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = min(La_SignificantM)
maxy = 20*max(La_SignificantM)
plot(thr,La_SignificantM[1,],ylim=c(miny,maxy),
main="Number of Discovered Features",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Number of Features",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(La_SignificantM))
{
lines(thr,La_SignificantM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", rownames(La_SignificantM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
meanDiscovered <- colMeans(La_SignificantM[fastRows,])
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[pearsonRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[spearmanRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[!T_BlindRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[T_BlindRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[corRankRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[maxCorRankRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[RLMfitMethod,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[!RLMfitMethod,]))
miny = min(meanDiscovered)
maxy = max(meanDiscovered) + 10
plot(thr,meanDiscovered[1,],ylim=c(miny,maxy),
main="Average Number of Discovered Features",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Number of Features",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(meanDiscovered))
{
lines(thr,meanDiscovered[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

SigDeM[is.na(SigDeM)] <- 0
miny = 1
maxy = 20*max(SigDeM)
plot(thr,SigDeM[1,],ylim=c(miny,maxy),
main="Number of Significant Latent Variables",
xlab="Correlation-Matrix's Maximum Goal",
ylab="How Many",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(SigDeM))
{
lines(thr,SigDeM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(SigDeM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
SigLatent <- colMeans(SigDeM[fastRows,])
SigLatent <- rbind(SigLatent,colMeans(SigDeM[pearsonRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[spearmanRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[!T_BlindRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[T_BlindRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[corRankRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[maxCorRankRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[RLMfitMethod,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[!RLMfitMethod,]))
miny = 1
maxy = max(SigLatent) + 10
plot(thr,SigLatent[1,],ylim=c(miny,maxy),
main="Average # of Significant Latent Variables",
xlab="Correlation-Matrix's Maximum Goal",
ylab="How Many",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(SigLatent))
{
lines(thr,SigLatent[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

sparcityM[is.na(sparcityM)] <- 0
miny = min(sparcityM)
maxy = max(sparcityM) + 0.75
plot(thr,sparcityM[1,],ylim=c(miny,maxy),
main="Matrix Sparcity",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Sparcity",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(sparcityM))
{
lines(thr,sparcityM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(sparcityM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
meanSparcity <- colMeans(sparcityM[fastRows,])
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[pearsonRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[spearmanRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[!T_BlindRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[T_BlindRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[corRankRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[maxCorRankRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[RLMfitMethod,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[!RLMfitMethod,]))
miny = min(meanSparcity)
maxy = max(meanSparcity)+0.25
plot(thr,meanSparcity[1,],ylim=c(miny,maxy),
main="Mean Matrix Sparcity",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Sparcity",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(meanSparcity))
{
lines(thr,meanSparcity[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)
